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          使用TensorFlow训练自己的语音识别AI
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        <p>  这次来训练一个基于CNN的语音识别模型。训练完成后，我们将尝试将此模型用于Hotword detection。</p>
<a id="more"></a>
<p>  人类是怎样听懂一句话的呢？以汉语为例，当听到“wo shi”的录音时，我们会想，有哪两个字是读作“wo shi”的，有人想到的是“我是”，也有人觉得是“我市”。<br>我们可以通过”wo shi”的频率的特征，匹配到一些结果，我们这次要训练的模型，也是基于频率特征的CNN模型。单纯的基于频率特征的识别有很大的局限性，比如前面提到的例子，光是听到“wo shi”可能会导致产生歧义，但是如果能有上下文，我们就可以大大提高“识别”的成功率。因此，类似Google Assistant那样的识别，不光是考虑到字词的发音，还联系了语义，就算有一两个字发音不清，我们还是能得到正确的信息。<br>但是基于频率特征的模型用作Hotword detection还是比较合适的，因为Horword通常是一两个特定的词，不需要联系语境进行语义分析。</p>
<h2 id="准备训练数据集"><a href="#准备训练数据集" class="headerlink" title="准备训练数据集"></a>准备训练数据集</h2><p>  开源的语言数据集比较少，这里我们使用TensorFlow和AIY团队推出的一个数据集，包含30个基本的英文单词的大量录音：<br><a href="https://download.tensorflow.org/data/speech_commands_v0.01.tar.gz" target="_blank" rel="noopener">下载地址</a><br>这个数据集只有1G多，非常小的语音数据集，不过用来实验是完全够的。</p>
<h2 id="运行docker并挂载工作目录"><a href="#运行docker并挂载工作目录" class="headerlink" title="运行docker并挂载工作目录"></a>运行docker并挂载工作目录</h2><p>新建一个speech_train文件夹，并在其中创建子文件夹dataset,logs,train,它们将用于存放数据集，log和训练文件。解压数据集到dataset，然后运行docker：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">docker run -it -v $(<span class="built_in">pwd</span>)/speech_train:/speech_train \</span><br><span class="line">  gcr.io/tensorflow/tensorflow:latest-devel</span><br></pre></td></tr></table></figure>
<h2 id="使用默认的conv模型开始训练"><a href="#使用默认的conv模型开始训练" class="headerlink" title="使用默认的conv模型开始训练"></a>使用默认的conv模型开始训练</h2><figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line"><span class="built_in">cd</span> /tensorflow/</span><br><span class="line">python tensorflow/examples/speech_commands/train.py \</span><br><span class="line">--data_dir=/speech_train/dataset/ \</span><br><span class="line">--summaries_dir=/speech_train/logs/ \</span><br><span class="line">--train_dir=/speech_train/train/ \</span><br><span class="line">--wanted_words=one,two,three,four,five,marvin</span><br></pre></td></tr></table></figure>
<p>在这里我们指定希望识别的label: one,two,three,four,five,marvin。数据集的其他部分将被归为<em>unknown</em></p>
<h2 id="使用TensorBoard使训练可视化"><a href="#使用TensorBoard使训练可视化" class="headerlink" title="使用TensorBoard使训练可视化"></a>使用TensorBoard使训练可视化</h2><p>我们可以通过分析生成的log使训练过程可视化：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><span class="line">tensorboard --logdir /speech_train/logs</span><br></pre></td></tr></table></figure>
<p>运行指令后，可以通过浏览器访问本地的6006端口进入TensorBoard。下图是使用conv模型完成18000 steps 训练的过程图：<br><img src="/2018-01-11/%E4%BD%BF%E7%94%A8TensorFlow%E8%AE%AD%E7%BB%83%E8%87%AA%E5%B7%B1%E7%9A%84%E8%AF%AD%E9%9F%B3%E8%AF%86%E5%88%ABAI/2018-01-16-08-48-12.png" alt><br>训练花了差不多15个小时。</p>
<h2 id="生成pb文件"><a href="#生成pb文件" class="headerlink" title="生成pb文件"></a>生成pb文件</h2><p>训练完成后，我们需要将其转化为pb文件：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">python tensorflow/examples/speech_commands/freeze.py \</span><br><span class="line">--start_checkpoint=/speech_train/train/conv.ckpt-18000 \</span><br><span class="line">--output_file=/speech_train/conv.pb \</span><br><span class="line">--wanted_words=one,two,three,four,five,marvin</span><br></pre></td></tr></table></figure>
<p>完成后，我们将得到一个名为conv.pb的文件，配合包含可识别label的txt文件就可以直接使用了。</p>
<h2 id="测试"><a href="#测试" class="headerlink" title="测试"></a>测试</h2><p>使用测试脚本进行测试：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><span class="line">python tensorflow/examples/speech_commands/label_wav.py \</span><br><span class="line">--graph=/speech_train/conv.pb \</span><br><span class="line">--labels=/speech_train/conv_labels.txt \</span><br><span class="line">--wav=/speech_train/dataset/marvin/0b40aa8e_nohash_0.wav</span><br></pre></td></tr></table></figure>
<p>训练的模型应能正确识别出marvin。</p>
<h2 id="使用准确度较低但是预测更快的low-latency-conv模型"><a href="#使用准确度较低但是预测更快的low-latency-conv模型" class="headerlink" title="使用准确度较低但是预测更快的low_latency_conv模型"></a>使用准确度较低但是预测更快的low_latency_conv模型</h2><p>  我们可以使用另外一种准确度较低但是预测更快的low_latency_conv模型进行训练：</p>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br></pre></td><td class="code"><pre><span class="line">python tensorflow/examples/speech_commands/train.py \</span><br><span class="line">--data_dir=/speech_train/dataset/ \</span><br><span class="line">--summaries_dir=/speech_train/logs/ \</span><br><span class="line">--train_dir=/speech_train/train/ \</span><br><span class="line">--model_architecture=low_latency_conv \</span><br><span class="line">--how_many_training_steps=20000,6000 \</span><br><span class="line">--learning_rate=0.01,0.001 \</span><br><span class="line">--wanted_words=one,two,three,four,marvin,wow</span><br></pre></td></tr></table></figure>
<p>当使用该模型时，可以适当增加training steps和learning rate。在这种情况下，训练的时间大大缩短了：<br><img src="/2018-01-11/%E4%BD%BF%E7%94%A8TensorFlow%E8%AE%AD%E7%BB%83%E8%87%AA%E5%B7%B1%E7%9A%84%E8%AF%AD%E9%9F%B3%E8%AF%86%E5%88%ABAI/2018-01-12-08-57-07.png" alt><br>只花了不到3小时.</p>
<h2 id="其他"><a href="#其他" class="headerlink" title="其他"></a>其他</h2><p>也可以使用gpu版本的tensorflow进行训练，速度可以提升不少哦。</p>

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